Pulse rate is widely recognized as an important cardio-vascular parameter for assessing a patient's health. Current methods of measuring pulse rate rely on the placement of electrodes on the patient's skin. Continuous monitoring using commercial pulse oximetry sensors attached to a finger or earlobe pose discomfort is susceptible to infections and could lead to irritation of the skin. In addition, these sensors must be placed and read by a qualified medical professional resulting in potential bottlenecks when attending to patients at healthcare facilities. Remote non-obtrusive monitoring is clearly an attractive alternative, provided that accurate measurements are obtained. Past attempts for remote non-obtrusive monitoring of the pulse rate include the use of PhotoPlethysmoGraphy (PPG). In PPG, a dedicated light source is used to capture the Blood Volume Pulse (BVP) by observing variations in reflected light due to pulsatile blood volume. Spectral estimation is then applied to the BVP to extract the pulse rate.
Recent work focused on obtaining the BVP using ambient light rather than a dedicated light source. This work pioneered an approach to extract pulse rate measurements and PPG signals using digital RGB cameras. The accuracy of such methods was improved on by applying blind source separation using Independent Component Analysis (ICA) and incorporating face tracking to automatically capture the face of a single or multiple patients. The work used a webcam to capture the video and was recently expanded to include measurements of other parameters such as the Heart Rate Variability (HRV) based on the ICA approach.
ICA is used to extract underlying statistical Independent Components (ICs) responsible for the observed signals. It assumes the observed signals are the result of a linear mixture of independent sources. The number of sources is equal to the number of observations, i.e., the linear mixture model is represented by a square matrix. Standard ICA techniques suffer from a sorting problem: the independent components are not ordered, meaning that the source signal of interest could be present in any of the ICA outputs. Previous work recognized the sorting problem and resolved it either by always selecting the second IC or by selecting the component for which the peak frequency has the highest power. In a recent contribution, constrained ICA (cICA) was used to improve the accuracy of BVP measurements using a webcam by solving the sorting problem of ICA.
Known methods rely on block processing, where a signal must be recorded for from 15 to 60 seconds and the only cardiac information obtained from the signal is the average heart rate during the recording. State-of-the-art methods depending on the use of ICA and cICA algorithms are computationally intensive and complex. This means that implementation must rely on a bulky and powerful computer and that latency in obtaining a result is inevitable. In addition, power consumption and heat dissipation are a concern when designing compact devices.